Currently, there are a variety of systems that can cluster data based on search terms. K-means and bisecting k-means are examples of algorithms that can be used to cluster data. These methods of clustering tend to be very processing intensive and are difficult to implement where there are rapid changes in the data set that is to be clustered. These clustering methods are not used to generate Boolean queries that can search a data set faster and with fewer processing resources than using existing clustering methods.
For example, U.S. Pat. No. 5,862,519 defines a system for “blind” clustering. The system takes data and segments the data into clusters. This system does not generate Boolean queries based on the clustered data and does not run the Boolean queries against a data set.
U.S. Patent Application Publication 2002/0087579 discloses a system for clustering data into classifications. The system then shows the relationships between data. The relationships can be various types of relationships, including Boolean relationships. However, this system does not generate any Boolean queries based on the clustered classifications. Moreover, the system does not run Boolean queries against the clustered classifications.